Identification of health behaviour clusters among people at high risk of stroke: A latent class profile analysis

Abstract Aims To identify the possible latent classes of health behaviour reported by people at high risk of stroke and to explore the predictors of these different classes of health behaviour. Design A cross‐sectional survey study. Methods A stratified cluster random sampling method was used to collect data from 2,500 individuals at high risk of stroke who were from Henan Province, China, from January 2018–January 2019. A latent class profile analysis was used to identify the health behaviour clusters and multinomial logistic regression was used to determine which factors predicted the emergent latent classes of health behaviour. Results High‐risk individuals ( N = 2,236) at high risk of stroke replied to the survey (89.44% response rate). Model fit indices (AIC = 257,509.610, BIC = 260,228.733, Entropy = 0.956) supported a three‐class model of health behaviours. The latent classes were Class 1 (a good level of adaptive health behaviour, 31%, N = 693), Class 2 (a moderate level of adaptive health behaviour, 36%, N = 805) and Class 3 (a poor level of adaptive health behaviour, 33%, N = 738); Based on physical and belief, behaviour and clinical profiles, the three classes were further labelled self‐realization deficiency subgroup, social contact anxiety subgroup and health responsibility absence subgroup respectively. Older age, male gender, no spouse, lower education and household income were risk factors associated with good health behaviour. After controlling these socio‐demographic variables, high levels of health‐related knowledge and attitude were the main positive predictors of health behaviour. Conclusions This study has identified three different latent classes of health behaviour and their predictive factors in people at high risk of stroke in the Chinese setting. Impact This study has significance for the promotion of adaptive health behaviour in individuals at high risk of stroke. It has allowed the identification of specific clusters of health behaviour that vary in terms of their adaptiveness and forms the basis for the development of a targeted intervention to promote health behaviour for each different subgroup.